Building the intelligence layer between businesses and advertising.
XEO is building an AI-native advertising system that understands the business behind every campaign, turns that context into better decisions, and progressively moves from recommendation to controlled execution.
Pre-launch. Core product architecture and decision infrastructure are actively being built and validated.
MVP / Decision System Development
Close the complete MVP decision loop with real campaign workflows.
One intelligence.
Three products.
XEO BUSINESS MEMORY
One business context
XEO
Advertising intelligence + controlled execution
DECIDE → EXECUTE → IMPROVE
Spend-linked commission
XEO PULSE
Continuous market intelligence
MONITOR → DETECT → UNDERSTAND → PRIORITIZE
Recurring subscription
XEO LEARN
Advertising education connected to the business
LEARN → APPLY → IMPROVE
Recurring subscription
Pre-launch.
But no longer just an idea.
Foundation
Closed / BuiltCore backend architecture, tenant isolation, entitlement foundations, engineering governance and test discipline.
Business understanding
Closed / Built foundationBusiness intake, evidence model, AI business analysis and re-analysis infrastructure.
Business memory
Active foundationPersistent business context and evidence lifecycle exist as part of the architecture; deeper outcome and attribution memory expands after real campaign workflows.
Decision system
CurrentCurrent product focus is closing the repeatable decision loop.
Safe execution
Next major layerAdvertising actions must execute only inside explicit permissions, approval boundaries and deterministic policy constraints.
Real campaign loop
Next proofReal businesses, real campaign workflows, outcome data, retention and willingness-to-pay evidence.
Multi-platform autonomy
LaterLimited Partner autonomy first; broader Operator-style autonomy only after evidence, attribution, safeguards and trust.
Stage states are qualitative on purpose. No completion percentages are claimed.
Built through gates,
not demos.
XEO development uses explicit acceptance gates rather than treating a visually working prototype as production completion. A gate closes only when a defined acceptance boundary has been independently reviewed.
V1 / V2 / V3
Backend / early product foundation
Foundational backend and early product layers reviewed and closed.
Gate 4.1
AI business analysis
Business analysis capability reviewed against a defined acceptance boundary.
Gate 4.2+
Evidence enrichment / true re-analysis
Implemented and validated through subsequent closure work.
Gate 4.4
Closure review
Closure review completed with no P0 / P1 / P2 findings.
Gate 4.5
V4 business understanding — final closure
Latest closure evidence: 12 / 12 closure criteria satisfied, 0 P0, 0 P1.
Engineering gate closed ≠ commercial MVP validated.
Closing Gate 4.5 does not mean the MVP is finished. It means one defined engineering boundary was met and reviewed.
Pulse already has
an operating intelligence pipeline.
XEO’s monitoring infrastructure has already been producing daily market-monitoring reports.
Closed
Not yet declared closed
Production and operational validation must satisfy the full gate before closure.
XEO does not mark infrastructure complete simply because it works once.
The company is being built across product, engineering and operations.
- XEO product architecture
- XEO Pulse product architecture
- XEO Learn product architecture
- Business Memory model
- Controlled autonomy model
- Monetization architecture
- Backend foundation
- Business intake
- AI business analysis
- Evidence enrichment
- True re-analysis
- Tenant-aware persistence
- Monitoring pipeline
- Test / gate discipline
- Investor deck
- Competitive analysis
- XEO overview
- Team & hiring plan
- Market & competitive landscape
- MVP-to-funding roadmap
- Moat narrative
- Monetization work
- Operating reports
- Founding-team recruiting active
- Investor outreach process active
- Customer discovery process
- AltaLab participation
- XEO HQ / internal document system
No completion percentages are assigned to this inventory.
The next milestone is not “more AI.” It is a complete decision loop.
Onboarding
Business context
Strategy
Prepared campaign
Approval
Execution
Report
New evidence
Next decision
↺ returns to business context
The next major proof for XEO is that this loop works with real businesses and real campaign workflows.
Architecture, business understanding, evidence lifecycle and gate-based validation. Built and reviewed.
- Real business data
- Real campaign workflows
- Outcome data
- User trust
- Retention signal
- Willingness to pay
Evidence in the right column is expected from the next stage, not claimed today.
One customer relationship.
Multiple revenue layers.
ONE BUSINESS
XEO CORE
Spend-linked commission
Customer advertising spend
↓XEO decision + execution layer
↓Applicable take rate
↓XEO revenue
Advertising budget itself is not XEO revenue. Only the applicable XEO commission is recognized as XEO revenue.
Intended structure: higher verified advertising spend → lower commission rate → potentially higher absolute revenue per customer.
Approximately 5% starting spend-linked commission, with the rate decreasing at higher spend / volume levels.
XEO PULSE
Recurring subscription
- Continuous market monitoring
- Change detection
- Business relevance
- Prioritization
- Actionable intelligence
XEO LEARN
Recurring subscription
- Structured advertising education
- Business-specific application
- Learning connected to real campaigns
Revenue per business
Potential revenue expansion. Expansion is not guaranteed.
Ad spend is not revenue.
$ Advertising budget
Funds committed by the customer for advertising.
Ad platform / pass-through ad funds
XEO commission
XEO revenue
Pulse subscription
XEO revenue
Learn subscription
XEO revenue
XEO separates customer advertising funds from earned platform revenue.
The relationship can deepen
without replacing the customer.
Business joins XEO
↓Uses core XEO
↓Runs more advertising
↓Adds Pulse
↓Adds Learn
↓Connects more platforms
↓Generates more decision history
Deeper XEO relationship
XEO has multiple possible monetization surfaces around the same underlying business relationship. These are potential expansion paths, not automatic customer behaviour.
The model is not the moat.
The memory can become one.
Business context
Decision history
Campaign outcomes
Market signals
Approval / trust history
BUSINESS MEMORY
↓Better context for the next decision
↺ decision result returns to business memory
The intended defensibility is not access to an LLM. It is accumulated business context, decision data, integration history, execution outcomes and closed-loop learning.
Intended compounding advantage — a potential data advantage as real usage accumulates. Not an established moat today.
Autonomy is earned,
not assumed.
Advisor
XEO recommends. Human approves execution.
Partner
XEO performs limited optimizations inside explicit boundaries.
Operator
Broader execution only after sufficient data, attribution, safeguards and trust.
Permissions
Spend limits
Approvals
Policy enforcement
Audit trail
Idempotency
Reconciliation
Rollback / compensation
Observability
Kill switch
The model does not receive unlimited advertising authority.
Each layer must prove
the one before it.
Timing is directional and milestone-dependent.
0–3 months
MVP / Advisor
- Onboarding
- Business context
- Advertising strategy recommendations
- Prepared launch
- First reports
3–6 months
Production core
- Real customers
- Stable backend
- First real campaign workflows
6–12 months
Attribution + memory
- Connect advertising decisions to customers
- Revenue
- Outcomes
- Decision history
12–18 months
Multi-platform + Partner
- Expand across X, Meta, Google and other validated platforms
- Limited autonomous optimization inside explicit boundaries
18–24+ months
Operator beta
- Budget allocator
- Opportunity Reserve
- Auditable Decision Log
- Broader controlled execution
Autonomy is a product outcome, not a starting feature.
Capital should remove
the next major risk.
Pre-seed
$2.0M
Current planning target — not committed capital
18–24 months to prove that XEO can make useful, trusted advertising decisions using real business data and real campaign workflows.
Engineering
→Stable production decision / execution system
Product
→Complete MVP loop
Integrations
→Real advertising workflows
Data / attribution
→Connect decisions to outcomes
Founding team
→Minimum team required to execute
Customer validation
→Initial customers, retention and willingness-to-pay evidence
No allocation percentages are claimed.
What the next round
is meant to prove.
01
Launch MVP
Initial design partners / customers
02
Run real campaign workflows
Collect real advertising outcome data
03
Prove trust
Users repeatedly accept and rely on useful recommendations
04
Prove willingness to pay
Real commercial evidence
05
Prove retention
Evidence that the system creates recurring value
06
Build attribution + deeper business memory
Connect decisions to customer / revenue / outcome history
07
Move toward Partner autonomy
Limited safe optimization inside explicit boundaries
The round is not for building the entire future XEO. It is for proving the first repeatable, trusted advertising decision system.
What exists.
What remains to prove.
- Architecture
- Engineering foundation
- Business-understanding infrastructure
- Evidence lifecycle
- Gate-based validation process
- Monitoring pipeline foundation
- Product family architecture
- Safety architecture
- Roadmap
- Business-model architecture
- Investor / operating documentation
- Real MVP users
- Real campaign workflows
- Outcome attribution
- Retention
- Willingness to pay
- Repeatable activation
- Production economics
- Validated commission tiers
- Validated Pulse pricing
- Validated Learn pricing
Company-building milestones.
AltaLab
Accepted into the program
Investor outreach
Active
Founding team
Recruiting underway across engineering, design and operations
Customer discovery
Active
Investor materials
Deck, competitive analysis, roadmap, team plan and supporting materials prepared
Program participation is company progress, not market traction.
Go deeper.
Public materials are available on this site. Everything else is shared on request — confidential documents are never exposed automatically.
Investor deck
Full company and product overview
REQUEST ACCESSCompetitive analysis
Market and competitive landscape
REQUEST ACCESSProduct architecture
Intelligence layer and product family
PUBLICTeam & hiring plan
Founding team structure and roles
PUBLICRoadmap
18–24+ month product trajectory
PUBLICMonetization model
Revenue architecture and pricing hypothesis
REQUEST ACCESSOperating report
Internal operating cadence
PRIVATETechnical status
Gate closure and engineering evidence
PUBLICThe next proof is simple.
Can XEO understand a real business, make a useful advertising decision, execute it safely, learn from the result, and make the next decision better?
That is the system XEO is building now.